Arguments

numBurnIn

A non-negative integer determining how many iterations the sampler should
skip before storing results. If missing or NA, the default is filled in from the sampler's
control object.

numSamples

A positive integer determining how many posterior samples should be
returned. If missing or NA, the default is also filled in from the control object.

updateState

A logical determining if the local cache of the sampler's state
should be updated after the completion of the run. If NA, the default is also
filled in from the control object.

shallow

A logical determining if the copy should retain the underlying data of the sampler
(TRUE) or have its own copies (FALSE).

control

An object inheriting from dbartsControl.

model

An object inheriting from dbartsModel.

data

An object inheriting from dbartsData.

y

A numeric response vector of length equal to that with which the sampler was created.

x

A numeric predictor vector of length equal to that with which the sampler was created. Can be
an entirely matrix of new number of rows for setTestPredictor.

x.test

A new matrix of test predictors, of the number of columns equal to that in the current model.

offset

A numeric vector of length equal to that with which the sampler was created, or NULL.
If offset.test was set from offset, will attempt to update that as well.

offset.test

A numeric vector of length equal to that of the test matrix, or NULL. Can be missing
for setTestPredictors.

column

An integer or character string vector specifying which column/columns of the predictor matrix is
to be replaced. If missing, the entire matrix is substitude.

treeNums

An integer vector listing the indices of the trees to print.

treeNum

An integer listing the indices of the tree to plot.

treePlotPars

A list containing the number quantities nodeHeight, nodeWidth, and
nodeGap, all of which control aspects of the resulting plot.

...

Extra arguments to plot.

Details

A dbartsSampler is a “mutable” object which contains information pertaining to
fitting a Bayesian additive regression tree model. The sampler is first created and then,
in a separate instruction, run or modified. In this way, MCMC samplers can be constructed
with BART components filling arbitrary roles.

Saving

saveing and loading a dbarts sampler for future use
requires that R's serialization mechanism be able to access the state of the sampler
which, for memory purposes, is only made available to R on request. To do this, one must
“touch” the sampler's state object before saving, e.g. for the object sampler,
execute invisible(sampler$state). This is in addition to guaranteeing that the
state object is not NULL, which can be done by setting the sampler's control
to an object with updateState as TRUE or passing TRUE as the
updateState argument to any of the sampler's applicable methods.

Value

For run, a named-list with contents sigma, train, test, and varcount.

For setPredictor, TRUE/FALSE depending on whether or not the operation was successful.
The operation can fail if the new predictor results in a tree with an empty leaf-node. If only single columns
were replaced, on the update is rolled-back so that the sampler remains in a valid state.

predict keeps the current test matrix in place and uses the current set of tree splits.
It is intended that this function only be used when the runMode of dbartsControl is
"fixedSamples", since otherwise only a single set of trees are stored.